code: def __init__(
      self, C_in, C_out, kernel_size, stride, padding, separable
  ):
      super(ConvBranch, self).__init__()
      self.preproc = StdConv(C_in, C_out)
      if separable:
          self.conv = SeparableConv(
              C_out, C_out, kernel_size, stride, padding
          )
      else:
          self.conv = nn.Conv2d(
              C_out, C_out, kernel_size, stride=stride, padding=padding
          )
      self.postproc = nn.Sequential(
          nn.BatchNorm2d(C_out, affine=False), nn.ReLU()
      )
  
imports: import torch
  import torch.nn
label_desc: Use image node_id 1 to create a new ConvBranch object from inherited base classes: nn with example: obj = ConvBranch(C_in, C_out, kernel_size, stride, padding, separable)